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Neuronal Dynamics:Computational Neuroscienceof Single NeuronsWeek 1 and Week 4:
Nonlinear Integrate-and-fire Model
Wulfram GerstnerEPFL, Lausanne, Switzerland
Nonlinear Integrate-and-fire (NLIF) - Definition - quadratic and expon. IF - Extracting NLIF model from data - exponential Integrate-and-fire - Extracting NLIF from detailed model - from two to one dimension
- Quality of NLIF?
Nonlinear Integrate-and-Fire Model
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Neuronal Dynamics – Review: Nonlinear Integrate-and Fire
)()( tRIuFudt
d
)()( tRIuuudt
drest
LIF (Leaky integrate-and-fire)
NLIF (nonlinear integrate-and-fire)
resetuu If firing:
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Neuronal Dynamics – 1.4. Leaky Integrate-and Fire revisited
LIF
ru u
If firing:
I=0u
dt
d
u
I>0u
dt
d
uresting
tu
repetitive
t
)()( tRIuuudt
drest
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Neuronal Dynamics – 1.4. Nonlinear Integrate-and Fire
)()( tRIuFudt
d
NLIF
ru ufiring:
I=0
ur
udt
dI>0u
dt
d
u
Nonlinear Integrate-and-Fire
rif u(t) = then r
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Nonlinear Integrate-and-fire Model
iui
r
)()( tRIuFudt
d
Fire+reset
NONlinear
threshold
Spike emission
resetI
j
F
rif u(t) = then
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Nonlinear Integrate-and-fire Model
)()( tRIuFudt
d
Fire+reset
NONlinear
threshold
I=0u
dt
d
ur
I>0u
dt
d
ur
Quadratic I&F:
02
12 )()( ccucuF
rif u(t) = then
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Nonlinear Integrate-and-fire Model
)()( tRIuFudt
d
Fire+reset
I=0u
dt
d
ur
I>0u
dt
d
ur
Quadratic I&F:
)exp()()( 0 ucuuuF rest
exponential I&F:0
212 )()( ccucuF
rif u(t) = then
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Neuronal Dynamics:Computational Neuroscienceof Single NeuronsWeek 1 and Week 4:
Nonlinear Integrate-and-fire Model
Wulfram GerstnerEPFL, Lausanne, Switzerland
Nonlinear Integrate-and-fire (NLIF) - Definition - quadratic and expon. IF - Extracting NLIF model from data - exponential Integrate-and-fire - Extracting NLIF from detailed model - from two to one dimension
Nonlinear Integrate-and-Fire Model
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( ) ( )du
f u R I tdt
What is a good choice of f ?
reset
r
If u
then reset to
u u
ru
Neuronal Dynamics – Review: Nonlinear Integrate-and-fire
See:week 1,lecture 1.5
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( ) ( )du
f u R I tdt
What is a good choice of f ?
(ii) Extract f from more complex models
(i) Extract f from data
reset rIf u then reset to u u (2)
(1)
Neuronal Dynamics – Review: Nonlinear Integrate-and-fire
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Neuronal Dynamics – 1.5. Inject current – record voltage
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Neuronal Dynamics – Inject current – record voltage
Badel et al., J. Neurophysiology 2008
voltage
u [mV]
)exp()()( u
restuuuFI(t)
1)()(
1uFtI
Cdt
du
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(i) Extract f from data
Pyramidal neuron Inhibitory interneuron
( )( )
f uf u
linear exponential
Badel et al. (2008)
Badel et al. (2008)
( ) exp( )rest
du uu u
dt
Exp. Integrate-and-Fire, Fourcaud et al. 2003
Neuronal Dynamics – Review: Nonlinear Integrate-and-fire
( ) ( )du
f u R I tdt
linear exponential
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( ) ( )du
f u R I tdt
Best choice of f : linear + exponential
reset rIf u then reset to u u
(1)
(2)
( ) exp( )rest
du uu u
dt
BUT: Limitations – need to add
-Adaptation on slower time scales-Possibility for a diversity of firing patterns-Increased threshold after each spike-Noise
Neuronal Dynamics – Review: Nonlinear Integrate-and-fire
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Neuronal Dynamics:Computational Neuroscienceof Single NeuronsWeek 1 and Week 4:
Nonlinear Integrate-and-fire Model
Wulfram GerstnerEPFL, Lausanne, Switzerland
Nonlinear Integrate-and-fire (NLIF) - Definition - quadratic and expon. IF - Extracting NLIF model from data - exponential Integrate-and-fire - Extracting NLIF from detailed model - from two to one dimension
Week 4 – part 5: Nonlinear Integrate-and-Fire Model
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Neuronal Dynamics – 4.5. Further reduction to 1 dimension
Separation of time scales-w is nearly constant (most of the time)
2-dimensional equation
( , ) ( )du
F u w RI tdt
stimulus
),( wuGdt
dww slow!
After reduction of HHto two dimensions:
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Crochet et al., 2011
awake mouse, cortex, freely whisking, Spontaneous activity in vivo
Neuronal Dynamics – 4.5 sparse activity in vivo
-spikes are rare events-membrane potential fluctuates around ‘rest’
Aims of Modeling: - predict spike initation times - predict subthreshold voltage
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)(),( tIwuFdt
du
stimulus
),( wuGdt
dww
0dt
du
0dt
dww
uI(t)=0
Stable fixed point
uw
Neuronal Dynamics – 4.5. Further reduction to 1 dimension
Separation of time scales
Flux nearly horizontal
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)(),( tIwuFdt
du
),( wuGdt
dww
0dt
dw
0dt
du
Stable fixed point
Neuronal Dynamics – 4.5. Further reduction to 1 dimensionHodgkin-Huxley reduced to 2dim
w u Separation of time scales
0w rest
dww w
dt
( , ) ( )rest
duF u w RI t
dt
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( ) ( )du
f u R I tdt
(i) Extract f from more complex models
( , ) ( )du
F u w R I tdt
),( wuGdt
dww
See week 3:2dim version of Hodgkin-Huxley
Separation of time scales:Arrows are nearly horizontal
resting state
restw wSpike initiation, from rest
A. detect spike and reset
B. Assume w=wrest
Neuronal Dynamics – Review: Nonlinear Integrate-and-fire
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(i) Extract f from more complex models
( , ) ( )du
F u w R I tdt
),( wuGdt
dww
Separation of time scalesrestw w
( , ) ( )rest
duF u w R I t
dt
linear exponential
See week 4:2dim version of Hodgkin-Huxley
Neuronal Dynamics – Review: Nonlinear Integrate-and-fire
( ) ( )du
f u R I tdt
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Neuronal Dynamics – 4.5. Nonlinear Integrate-and-Fire Model
Image: Neuronal Dynamics, Gerstner et al., Cambridge Univ. Press (2014)
( , ) ( ) ( ) ( )rest
duF u w RI t f u RI t
dt
Nonlinear I&F (see week 1!)
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Neuronal Dynamics – 4.5. Nonlinear Integrate-and-Fire Model
Image: Neuronal Dynamics, Gerstner et al., Cambridge Univ. Press (2014)
( , ) ( ) ( ) ( )rest
duF u w RI t f u RI t
dt
Nonlinear I&F (see week 1!)
( ) ( ) exp( )urestf u u u
Exponential integrate-and-fire model (EIF)
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Neuronal Dynamics – 4.5. Exponential Integrate-and-Fire Model
Image: Neuronal Dynamics, Gerstner et al., Cambridge Univ. Press (2014)
( ) ( ) exp( )urestf u u u
Exponential integrate-and-fire model (EIF)
linear
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Neuronal Dynamics – 4.5. Exponential Integrate-and-Fire Model
3 4( ) ( ) ( ) ( )Na Na K K l l
duC g m h u E g n u E g u E I tdt
3 40[ ( )] ( ) [ ] ( ) ( ) ( )Na rest Na K rest K l l
duC g m u h u E g n u E g u E I tdt
( , , ) ( ) ( ) ( )rest rest
duF u h n RI t f u RI t
dt
( ) ( ) exp( )urestf u u u
gives expon. I&F
Direct derivation from Hodgkin-Huxley
Fourcaud-Trocme et al, J. Neurosci. 2003
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Neuronal Dynamics – 4.5. Nonlinear Integrate-and-Fire Model
Separation of time scales-w is constant (if not firing)
2-dimensional equation
( , ) ( )du
F u w RI tdt
),( wuGdt
dww
( ) ( )du
f u RI tdt
Relevant during spike and downswing of AP
threshold+reset for firing
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Neuronal Dynamics – 4.5. Nonlinear Integrate-and-Fire Model
Separation of time scales-w is constant (if not firing)
2-dimensional equation
( , ) ( )du
F u w RI tdt
),( wuGdt
dww
( ) ( )du
f u RI tdt
Linear plus exponential
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Neuronal Dynamics:Computational Neuroscienceof Single NeuronsWeek 1 and Week 4:
Nonlinear Integrate-and-fire Model
Wulfram GerstnerEPFL, Lausanne, Switzerland
Nonlinear Integrate-and-fire (NLIF) - Definition - quadratic and expon. IF - Extracting NLIF model from data - exponential Integrate-and-fire - Extracting NLIF from detailed model - from two to one dimension
- Quality of NLIF?
Nonlinear Integrate-and-Fire Model
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Crochet et al., 2011
awake mouse, cortex, freely whisking, Spontaneous activity in vivo
Neuronal Dynamics – 4.5 sparse activity in vivo
-spikes are rare events-membrane potential fluctuates around ‘rest’
Aims of Modeling: - predict spike initation times - predict subthreshold voltage
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Neuronal Dynamics – 4.5.How good are integrate-and-fire models?
Aims: - predict spike initation times - predict subthreshold voltage
Badel et al., 2008
Add adaptation andrefractoriness (week 7)
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Neuronal Dynamics – Quiz 4.7.A. Exponential integrate-and-fire model. The model can be derived[ ] from a 2-dimensional model, assuming that the auxiliary variable w is constant.[ ] from the HH model, assuming that the gating variables h and n are constant.[ ] from the HH model, assuming that the gating variables m is constant.[ ] from the HH model, assuming that the gating variables m is instantaneous.
B. Reset. [ ] In a 2-dimensional model, the auxiliary variable w is necessary to implement a reset of the voltage after a spike[ ] In a nonlinear integrate-and-fire model, the auxiliary variable w is necessary to
implement a reset of the voltage after a spike[ ] In a nonlinear integrate-and-fire model, a reset of the voltage after a spike is
implemented algorithmically/explicitly
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Neuronal Dynamics – Nonlinear Integrate-and-FireReading: W. Gerstner, W.M. Kistler, R. Naud and L. Paninski,Neuronal Dynamics: from single neurons to networks and models of cognition. Chapter 4: Introduction. Cambridge Univ. Press, 2014OR W. Gerstner and W.M. Kistler, Spiking Neuron Models, Ch.3. Cambridge 2002OR J. Rinzel and G.B. Ermentrout, (1989). Analysis of neuronal excitability and oscillations. In Koch, C. Segev, I., editors, Methods in neuronal modeling. MIT Press, Cambridge, MA.
Selected references.-Ermentrout, G. B. (1996). Type I membranes, phase resetting curves, and synchrony. Neural Computation, 8(5):979-1001.-Fourcaud-Trocme, N., Hansel, D., van Vreeswijk, C., and Brunel, N. (2003). How spike generation mechanisms determine the neuronal response to fluctuating input.
J. Neuroscience, 23:11628-11640.-Badel, L., Lefort, S., Berger, T., Petersen, C., Gerstner, W., and Richardson, M. (2008). Biological Cybernetics, 99(4-5):361-370.
- E.M. Izhikevich, Dynamical Systems in Neuroscience, MIT Press (2007)